Differential proteomic analysis of noncardia gastric cancer from individuals of northern Brazil

Gastric cancer is the second leading cause of cancer-related death worldwide. The identification of new cancer biomarkers is necessary to reduce the mortality rates through the development of new screening assays and early diagnosis, as well as new target therapies. In this study, we performed a proteomic analysis of noncardia gastric neoplasias of individuals from Northern Brazil. The proteins were analyzed by two-dimensional electrophoresis and mass spectrometry. For the identification of differentially expressed proteins, we used statistical tests with bootstrapping resampling to control the type I error in the multiple comparison analyses. We identified 111 proteins involved in gastric carcinogenesis. The computational analysis revealed several proteins involved in the energy production processes and reinforced the Warburg effect in gastric cancer. ENO1 and HSPB1 expression were further evaluated. ENO1 was selected due to its role in aerobic glycolysis that may contribute to the Warburg effect. Although we observed two up-regulated spots of ENO1 in the proteomic analysis, the mean expression of ENO1 was reduced in gastric tumors by western blot. However, mean ENO1 expression seems to increase in more invasive tumors. This lack of correlation between proteomic and western blot analyses may be due to the presence of other ENO1 spots that present a slightly reduced expression, but with a high impact in the mean protein expression. In neoplasias, HSPB1 is induced by cellular stress to protect cells against apoptosis. In the present study, HSPB1 presented an elevated protein and mRNA expression in a subset of gastric cancer samples. However, no association was observed between HSPB1 expression and clinicopathological characteristics. Here, we identified several possible biomarkers of gastric cancer in individuals from Northern Brazil. These biomarkers may be useful for the assessment of prognosis and stratification for therapy if validated in larger clinical study sets.     

Characterization of the human gastric fluid proteome reveals distinct pH-dependent protein profiles: implications for biomarker studies

Gastric fluid is a source of gastric cancer biomarkers. However, very little is known about the normal gastric fluid proteome and its biological variations. In this study, we performed a comprehensive analysis of the human gastric fluid proteome using samples obtained from individuals with benign gastric conditions. Gastric fluid proteins were prefractionated using ultracentrifuge filters (3 kDa cutoff) and analyzed by two-dimensional gel electrophoresis (2-DE) and multidimensional LC-MS/MS. Our 2-DE analysis of 170 gastric fluid samples revealed distinct protein profiles for acidic and neutral samples, highlighting pH effects on protein composition. By 2D LC-MS/MS analysis of pooled samples, we identified 284 and 347 proteins in acidic and neutral samples respectively (FDR ≤1%), of which 265 proteins (72.4%) overlapped. However, unlike neutral samples, most proteins in acidic samples were identified from peptides in the filtrate (i.e., <3 kDa). Consistent with this finding, immunoblot analysis of six potential gastric cancer biomarkers rarely detected full-length proteins in acidic samples. These findings have important implications for biomarker studies because a majority of gastric cancer patients have neutral gastric fluid compared to noncancer controls. Consequently, sample stratification, choice of proteomic approaches, and validation strategy can profoundly affect the interpretation of biomarker findings. These observations should help to refine gastric fluid biomarker studies.     

Discovery of Tumor Markers for Gastric Cancer by Proteomics

Gastric cancer (GC) has a high rate of morbidity and mortality among various cancers worldwide. The development of noninvasive diagnostic methods or technologies for tracking the occurrence of GC is urgent, and searching reliable biomarkers is considered.This study intended to directly discover differential biomarkers from GC tissues by two-dimension-differential gel electrophoresis (2D-DIGE), and further validate protein expression by western blotting (WB) and immunohistochemistry (IHC).Pairs of GC tissues (gastric cancer tissues and the adjacent normal tissues) obtained from surgery was investigated for 2D-DIEG.Five proteins wereconfirmed by WB and IHC, including glucose-regulated protein 78 (GRP78), glutathione s-transferase pi (GSTpi), apolipoprotein AI (ApoAI), alpha-1 antitrypsin (A1AT) and gastrokine-1 (GKN-1). Among the results, GRP78, GSTpi and A1ATwere significantlyup-regulated and down-regulated respectively in gastric cancer patients. Moreover, GRP78 and ApoAI were correlated with A1AT for protein expressions.This study presumes these proteins could be candidates of reliable biomarkers for gastric cancer.     

Identification and validation of novel candidate protein biomarkers for the detection of human gastric cancer

The timely detection of gastric cancer will contribute significantly towards effective treatment and is aided by the availability and reliability of appropriate biomarkers. A combination of several biomarkers can improve the sensitivity and specificity of cancer detection and this work reports results from a panel of 4 proteins. By combining a validated preclinical mouse model with a proteomic approach we have recently discovered novel biomarkers for the detection of gastric cancer. Here, we investigate the specificity of four of those biomarkers (afamin, clusterin, VDBP and haptoglobin) for the detection of gastric cancer using two independent methods of validation: ELISA, and a non antibody based method: Multiple Reaction Monitoring with High Resolution Mass Spectrometry (MRM-HR). All four biomarkers reliably differentiated GC from benign patient serum, and also in a small cohort of 11 early stage cases. We also present a novel isoform specific biomarker alpha-1-antitrypsin (A1AT) that was identified using a mouse model for gastric cancer. This isoform is distinct in charge and mobility in a pH gradient and was validated using human samples by isoelectric focussing and Western-blot (IEF-WB). This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge.     

Quantitative proteomics identified 3 oxidative phosphorylation genes with clinical prognostic significance in gastric cancer

The aim of the present study was to explore the underlying mechanisms involved in gastric cancer (GC) formation using data‐independent acquisition (DIA) quantitative proteomics analysis. We identified the differences in protein expression and related functions involved in biological metabolic processes in GC. Totally, 745 differentially expressed proteins (DEPs) were found in GC tissues vs. gastric normal tissues. Despite enormous complexity in the details of the underlying regulatory network, we find that clusters of proteins from the DEPs were mainly involved in 38 pathways. All of the identified DEPs involved in oxidative phosphorylation were down‐regulated. Moreover, GC possesses significantly altered biological metabolic processes, such as NADH dehydrogenase complex assembly and tricarboxylic acid cycle, which is mostly consistent with that in KEGG analysis. Furthermore the higher expression of UQCRQ, NDUFB7 and UQCRC2 were positively correlated with a better prognosis, implicating these proteins may as novel candidate diagnostic and prognostic biomarkers.     

Diverse proteomic alterations in gastric adenocarcinoma

Gastric adenocarcinoma is one of the most common cancers in Asian countries including China. Although its incidence rates in the West are lower than that in Asia, gastric cancer is still a major health problem worldwide, being second only to lung cancers in the number of deaths it causes. Helicobacter pylori infection has been identified as the major pathogen, but the detailed pathogenesis of gastric carcinoma remains elusive. Due to the lack of suitable and specific biomarkers for early detection, most cases of the disease are diagnosed at late stages and the survival rate is low. In this study, we used a proteomic approach to globally analyze the protein profiles of paired surgical specimens of primary gastric adenocarcinoma and nontumor mucosa aiming at identifying specific disease-associated proteins as potential clinical biomarkers and for carcinogenetic study. Compared to nontumor tissues, multiple protein alterations were found in tumor tissues. Some of these alterations involve variations in the expression of cytoskeleton proteins, including an increase in cytokeratin 8 and tropomyosin isoform and a decrease in cytokeratin 20. Co-up-regulations of heat-shock proteins and glycolytic enzymes were observed in tumor tissues, indicating self-protective efforts of cells and the growing energy requirement during malignant transformation. Diverse regulations also occurred with proteins involved in cell proliferation and differentiation, such as GMP reductase 2 and creatine kinase B, and proteins bearing potential tumor suppressor activities, including prohibitin and selenium binding protein 1. More interestingly, a human stomach-specific protein, 18 kDa antrum mucosa protein, was found to be dramatically under-expressed in cancer tissues, implicating a possible special pathological role for this protein in gastric carcinogenesis. Further comprehensive evaluation by globally considering the altered factors may result in the discovery of a biomarker index for effective assessment of the disease and may provide in-depth information for better understanding the pathogenesis of gastric cancer.     

Low abundance protein enrichment for discovery of candidate plasma protein biomarkers for early detection of breast cancer

Molecular biomarkers of early stage breast cancer may improve the sensitivity and specificity of diagnosis. Plasma biomarkers have additional value in that they can be monitored with minimal invasiveness. Plasma biomarker discovery by genome-wide proteomic methods is impeded by the wide dynamic range of protein abundance and the heterogeneity of protein expression in healthy and disease populations which requires the analysis of a large number of samples. We addressed these issues through the development of a novel protocol that couples a combinatorial peptide ligand library protein enrichment strategy with isobaric label-based 2D LC-MS/MS for the identification of candidate biomarkers in high throughput. Plasma was collected from patients with stage I breast cancer or benign breast lesions. Low abundance proteins were enriched using a bead-based combinatorial library of hexapeptides. This resulted in the identification of 397 proteins, 22% of which are novel plasma proteins. Twenty-three differentially expressed plasma proteins were identified, demonstrating the effectiveness of the described protocol and defining a set of candidate biomarkers to be validated in independent samples. This work can be used as the basis for the design of properly powered investigations of plasma protein expression for biomarker discovery in larger cohorts of patients with complex disease.     

Discovery of biomarkers for gastric cancer: a proteomics approach

Gastric cancer is the second leading cause of cancer-related deaths worldwide. Although many treatment options exist for patients with gastric tumors, the incidence and mortality rate of gastric cancer are on the rise. The early stages of gastric cancer are non-symptomatic, and the treatment response is unpredictable. This situation is further aggravated by a lack of diagnostic biomarkers that can aid in the early detection and prognosis of gastric cancer and in the prediction of chemoresistance. Moreover, clinical surgical specimens are rarely obtained, and traditional biomarkers of gastric cancer are not very effective. Many studies in the field of proteomics have contributed to the discovery and establishment of powerful diagnostic tools (e.g., ProteinChip array) in the management of cancer. The evolution in proteomic technologies has not only enabled the screening of a large number of samples but also enabled the identification of pathologically significant proteins, such as phosphoproteins, and the quantitation of difference in protein expression under different conditions. Multiplexed assays are used widely to accurately fractionate various complex samples such as blood, tissue, cells, and Helicobacter pylori-infected specimens to identify differentially expressed proteins. Biomarker detection studies have substantially contributed to the areas of secretome, metabolome, and phosphoproteome. Here, we review the development of potential biomarkers in the natural history of gastric cancer, with specific emphasis on the characteristics of target protein convergence.     

Low expression of lncRNA APTR promotes gastric cancer progression

BackgroundGastric cancer (GC) is one of the most common cancers worldwide and the majority of GC patients are diagnosed at advanced stages due to the lack of early detection biomarkers. LncRNAs have been shown to play important roles in various diseases and could be predictive biomarkers and therapeutic targets. Our study demonstrated that low expression of lncRNA APTR could promote gastric cancer progression.MethodsDifferentiated expressed lncRNAs were identified through analyzing TCGA paired GC RNA sequencing data. LncRNA APTR's clinical relevance was analyzed using the TCGA dataset and GEO datasets. APTR expression in patient samples was detected through qPCR. The proliferation, colony formation, and migration of GC cells were tested. Bioinformatic analyses were performed to explore APTR-affected signaling pathways in GC.ResultsLncRNA APTR is lower expressed in gastric tumor samples and low expression of APTR predicts a poor diagnosis and outcome in GC patients. Silencing APTR promotes gastric cancer proliferation and invasiveness. APTR expression is negatively correlated with inflammatory signaling in the gastric tumor microenvironment.ConclusionOur study showed that low expression of lncRNA APTR in gastric cancer is correlated with tumorigenesis and poor diagnosis and prognosis, which is a potential biomarker for gastric cancer patients' diagnosis and treatment.     

Construction of a three-component regulatory network of transcribed ultraconserved regions for the identification of prognostic biomarkers in gastric cancer

Altered expression and functional roles of the transcribed ultraconserved regions (T-UCRs), as genomic sequences with 100% conservation between the genomes of human, mouse, and rat, in the pathophysiology of neoplasms has already been investigated. Nevertheless, the relevance of the functions for T-UCRs in gastric cancer (GC) is still the subject of inquiry. In the current study, we first used a genome-wide profiling approach to analyze the expression of T-UCRs in GC patients. Then, we constructed a three-component regulatory network and investigated potential diagnostic and prognostic values of the T-UCRs. The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD) dataset was used as a resource for the RNA-sequencing data. FeatureCounts was utilized to quantify the number of reads mapped to each T-UCR. Differential expression analysis was then conducted using DESeq2. In the following, interactions between T-UCRs, microRNAs (miRNAs), and messenger RNAs (mRNAs) were combined into a three-component network. Enrichment analyses were performed and a protein–protein interaction (PPI) network was constructed. The R Survival package was utilized to identify survival-related significantly differentially expressed T-UCRs (DET-UCRs). Using an in-house cohort of GC tissues, expression of two DET-UCRs was furthermore experimentally verified. Our results showed that several T-UCRs were dysregulated in TCGA-STAD tumoral samples compared to nontumoral counterparts. The three-component network was constructed which composed of DET-UCRs, miRNAs, and mRNAs nodes. Functional enrichment and PPI network analyses revealed important enriched signaling pathways and gene ontologies such as “pathway in cancer” and regulation of cell proliferation and apoptosis. Five T-UCRs were significantly correlated with the overall survival of GC patients. While no expression of uc.232 was observed in our in-house cohort of GC tissues, uc.343 showed an increased expression, although not statistically significant, in gastric tumoral tissues. The constructed three-component regulatory network of T-UCRs in GC presents a comprehensive understanding of the underlying gene expression regulation processes involved in tumor development and can serve as a basis to investigate potential prognostic biomarkers and therapeutic targets.     

Evaluation of p38 MAPK pathway as a molecular signature in ulcerative colitis

Early diagnosis and treatment of ulcerative colitis (UC) is clinically challenging. To overcome this problem, we explored the interrelated multiplex signaling pathway to identify molecular signatures in UC by using integrated strategy in proteomics. Intestinal mucosa of 12 UC cases and 12 normal controls underwent comparative proteomic analysis. A total of 26 unique differential proteins were identified, including 12 up-regulated and 14 down-regulated in UC group. A differential protein cluster, consisting of 11 proteins involved in p38 mitogen-activated protein kinase (MAPK) pathway, was deduced and validated by Western blot. Furthermore, three proteins elicited from the protein cluster, phosphorylated p38, MAWBP and galectin-3, as a molecular signature, were analyzed by immunohistochemistry on 118 UC and normal samples. Increased expression of P-p38 and down-regulated MAWBP and/or galectin-3 were detected in UC compared to normal samples (p < 0.001). This signature correlated with disease progression of UC (p < 0.01), and classified UC risk with high sensitivity (94.83 ± 2.91%) and specificity (98.33 ± 1.65%). In addition, P38 MAPK pathway modulated the expression of the protein clusters in macrophage cell line as evidenced by the alteration with specific inhibitor SB203580. These results indicate that molecular signature of P38 MAPK pathway might be a potential biomarker for evaluating UC risk.     

A controversial tumor marker: is SM22 a proper biomarker for gastric cancer cells?

SM22, a dominant protein in smooth muscle cells (SMCs), has been widely reported to be abnormally expressed in many solid tumors. However, the expression patterns of SM22 are not consistent in all tumors, not even in the same ones. Whether SM22 should be considered a tumor biomarker is still debated in different laboratories. Herein, we have carried out a systematical investigation to validate SM22 expression in the primary tissues of gastric cancer (GC). Of eight cases, seven samples were found in the elevated expression of SM22 proteins through proteomic analysis. The observation was further verified by the approaches of Western blotting and quantitative RT-PCR. Surprisingly, the results achieved from tissue microarray in 126 GC cases appeared contrary to the proteomic conclusion, in which the highly expressed SM22 was mainly found in smooth muscle layers, blood vessels, and myofibroblasts. This suggested that the increased abundance of SM22 in the cancerous regions was not caused by the presence of the GC cells. Furthermore, the expression of SM22 was measured in different GC cell lines and SMCs with Western blotting and quantitative RT-PCR. The results revealed that SM22 expression in SMCs was dramatically higher than that of the GC cells, which indicates that SM22 is unlikely to be a proper biomarker for GC. Instead, it can be considered a potential indicator for the abnormal developments of smooth muscles, blood vessels, or myofibroblasts triggered by tumorigenesis.     

Proteomic analysis of cancer tissues: shedding light on carcinogenesis and possible biomarkers

Lung, gastric, colorectal, pancreatic, and esophageal cancers, as well as hepatocellular carcinoma (HCC), were the six most common and highly fatal cancers for Japanese men in Japan in 2003, while for women uterine cervical cancer could also be added to this list. To identify diagnostic or therapeutic biomarkers for these cancers, investigators are nowadays performing proteomic analyses of cancer tissues and cells, and revealing a large number of molecules which are diagnostic, prognostic and informative of carcinogenesis. From reports of proteomic analyses of cancerous tissues and noncancerous tissues sampled from HCC, and pancreatic, esophageal, gastric, colorectal, lung and uterine cervical cancers, we classified the proteins into digestive enzymes, growth factors, cell adhesion molecules, calcium-binding proteins, proteases, protease inhibitors, transporter proteins, structural molecules, apoptosis inhibitor, molecular chaperone, as well as proteins related to cell growth, cell differentiation, cell transformation, tumor invasion, carcinogen metabolism, and others. The aim of this study was to understand carcinogenesis of major cancers from a proteomics perspective using samples from cancer patients, and to elucidate their tumor biomarkers.     

Upregulation of plasma C9 protein in gastric cancer patients

Gastric cancer is one of the leading causes of cancer‐related deaths worldwide. Current biomarkers used in the clinic do not have sufficient sensitivity for gastric cancer detection. To discover new and better biomarkers, protein profiling on plasma samples from 25 normal, 15 early‐stage and 21 late‐stage cancer was performed using an iTRAQ‐LC‐MS/MS approach. The level of C9 protein was found to be significantly higher in gastric cancer compared with normal subjects. Immunoblotting data revealed a congruent trend with iTRAQ results. The discriminatory power of C9 between normal and cancer states was not due to inter‐patient variations and was independent from gastritis and Helicobacter pylori status of the patients. C9 overexpression could also be detected in a panel of gastric cancer cell lines and their conditioned media compared with normal cells, implying that higher C9 levels in plasma of cancer patients could be attributed to the presence of gastric tumor. A subsequent blind test study on a total of 119 plasma samples showed that the sensitivity of C9 could be as high as 90% at a specificity of 74%. Hence, C9 is a potentially useful biomarker for gastric cancer detection.     

Rat plasma proteomics: effects of abundant protein depletion on proteomic analysis

The proteomic analysis of plasma and serum samples represents a formidable challenge due to the presence of a few highly abundant proteins such as albumin and immunoglobulins. Detection of low abundance protein biomarkers requires therefore either the specific depletion of high abundance proteins with immunoaffinity columns and/or optimized protein fractionation methods based on charge, size or hydrophobicity. Here we describe the depletion of seven abundant rat plasma proteins with an immunoaffinity column with coupled antibodies directed against albumin, IgG, transferrin, IgM, haptoglobin, fibrinogen and alpha1-anti-trypsin. The IgY-R7-LC2 (Beckman Coulter) column showed high specificity for the targeted proteins and was able to efficiently remove most of the albumin, IgG and transferrin from rat plasma samples as judged by Western blot analysis. Depleted rat plasma protein samples were analyzed by SELDI-TOF MS, 2D SDS-PAGE and 2D-LC and compared to non-depleted plasma samples as well as to the abundant protein fraction that was eluted from the immunoaffinity column. Analysis of the depleted plasma protein fraction revealed improved signal to noise ratios, regardless of which proteomic method was applied. However, only a small number of new proteins were observed in the depleted protein fraction. Immunoaffinity depletion of abundant plasma proteins results in the significant dilution of the original sample which complicates subsequent analysis. Most proteomic approaches require specialized sample preparation procedures during which significant losses of less abundant proteins and potential biomarkers can occur. Even though abundant protein depletion reduces the dynamic range of the plasma proteome by about 2-3 orders of magnitude, the difference between medium-abundant and low abundant plasma proteins is still in the range of 7-8 orders of magnitude and beyond the dynamic range of current proteomic technologies. Thus, exploring the plasma proteome in greater detail remains a daunting task.     

Proteomic identification of differentially-expressed genes in human gastric carcinomas

Although genetic alterations in proto-oncogenes, tumor-suppressor genes, cell cycle regulators, and cell growth factors have been implicated in the process of human gastric carcinogenesis, the principle carcinogenic mechanisms are not fully understood. In this study, we used a proteomic approach to search for genes that may be involved in gastric carcinogenesis and that might serve as diagnostic markers. We identified nine proteins with increased expression and 13 proteins with decreased expression in gastric carcinomas. The two most notable groups included proteins involved in mitotic checkpoint (MAD1L1 and EB1) and mitochondrial functions (CLPP, COX5A, and ECH1). This suggested that there are links between dysfunctions in these processes and gastric carcinogenesis. We also observed the differential expression of HSP27 and CYR61 proteins in gastric carcinoma, whose expression is known to be altered in other types of tumors. Furthermore, the study identified proteins whose function in gastric carcinomas was previously unsuspected and that may serve as new molecular markers for gastric carcinomas. Importantly, immunohistochemical analyses confirmed that the levels of expression of MAD1L1, HSP27, and CYR61 were altered in gastric carcinoma tissues. Therefore, our study suggested not only that the proteins identified in this study can be useful diagnostic markers but also that a proteomics-based approach is useful for developing a more complete picture of the pathogenesis and function of gastric carcinomas.